Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools

Smith, Wade A.; Borghesani, Pietro; Ni, Qing; Wang, Kesheng; Peng, Zhongxiao

doi:10.1016/j.ymssp.2019.106303

Cited by 112 publications

(71 citation statements)

References 26 publications

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“…When a fast kurtogram is used in the study of compound fault characteristics, it can effectively locate the low-density impact fault subband and the high-impact density fault subband will be submerged [26]. To compensate for this defect, a protrugram in 1/3-binary tree form [27] is used in this algorithm. is algorithm can locate the high-impact density fault subband in the experiment.…”

Section: Dual-kurtogram Algorithmmentioning

confidence: 99%

An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Jiao

Fan

2021

Shock and Vibration

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Condition monitoring and compound fault diagnosis are crucial key points for ensuring the normal operation of rotating machinery. A novel method for condition monitoring and compound fault diagnosis based on the dual-kurtogram algorithm and multivariate statistical process control is established in this study. The core idea of this method is the capability of the dual-kurtogram in extracting subbands. Vibration data under normal conditions are decomposed by the dual-kurtogram into two subbands. Then, the spectral kurtosis (SK) of Subband I and the envelope spectral kurtosis (ESK) of Subband II are formulated to construct a control limit based on kernel density estimation. Similarly, vibration data that need to be monitored are constructed into two subbands by the dual-kurtogram. The SK of Subband I and the ESK of Subband II are calculated to derive T 2 statistics based on the covariance determinant. An alarm will be triggered when the T 2 statistics exceed the control limit and suitable subbands for square envelope analysis are adopted to obtain the characteristic frequency. Simulation and experimental data are used to verify the feasibility of the proposed method. Results confirm that the proposed method can effectively perform condition monitoring and fault diagnosis. Furthermore, comparison studies show that the proposed method outperforms the traditional T 2 control chart, envelope analysis, and empirical mode decomposition.

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Section: Dual-kurtogram Algorithmmentioning

confidence: 99%

An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Jiao

Fan

2021

Shock and Vibration

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“…The envelope spectrum is commonly used in the bearing diagnosis conducted by an expert [39]. However, the automated selection of an appropriate frequency band for demodulation is complicated [33,40], and therefore, the amplitude spectrum was used in this study instead. The computational bearing frequencies (see Table 1) exhibit some uncertainty due to the measurement precision, skidding and variations in the rotational speed.…”

Section: No Feature Details Generalized Norm (L 10 )mentioning

confidence: 99%

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

et al. 2021

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Drill ships and offshore rigs use azimuth thrusters for propulsion, maneuvering and steering, attitude control and dynamic positioning activities. The versatile operating modes and the challenging marine environment create demand for flexible and practical condition monitoring solutions onboard. This study introduces a condition monitoring algorithm using acceleration and shaft speed data to detect anomalies that give information on the defects in the driveline components of the thrusters. Statistical features of vibration are predicted with linear regression models and the residuals are then monitored relative to multivariate normal distributions. The method includes an automated shaft speed selection approach that identifies the normal distributed operational areas from the training data based on the residuals. During monitoring, the squared Mahalanobis distance to the identified distributions is calculated in the defined shaft speed ranges, providing information on the thruster condition. The performance of the method was validated based on data from two operating thrusters and compared with reference classifiers. The results suggest that the method could detect changes in the condition of the thrusters during online monitoring. Moreover, it had high accuracy in the bearing condition related binary classification tests. In conclusion, the algorithm has practical properties that exhibit suitability for online application.

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“…The most popular approach is the envelope analysis and detection of fault frequencies in the spectrum of the envelope for a pre-filtered signal. The modified versions of the envelope are also known, e.g., squared envelope spectrum (SES) [2,7], log-envelope spectrum (LES) [8,9]. The filtration of a raw signal is used to select its informative part and avoid other spectral content not related to the local damage.…”

Section: Introductionmentioning

confidence: 99%

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

2020

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The vibration signals acquired on machines usually have complex spectral structure. As the signal of interest (SOI) is weak (especially at an early stage of damage) and covers some frequency range (around structural resonance), it requires its extraction from a raw observation. Until now, most of the techniques assumed the presence of Gaussian noise. Unfortunately, there are cases when the non-informative part of the signal (considered as the noise) is non-Gaussian due to the random disturbances or nature of the process executed by the machine. Thus, the problem can be formulated as the extraction of the SOI from the non-Gaussian noise. Recently this problem has been recognized by several authors and some new ideas have been developed. In this paper, we would like to compare these techniques for benchmark signals (Gaussian noise, cyclic impulsive signals, non-cyclic impulsive signals with random amplitudes and locations of impulses and a mixture of all of them). Our analysis will cover spectral kurtosis, kurtogram, stability index (Alpha selector), conditional variance-based selector, spectral Gini index, spectral smoothness index and infogram. Finally, a discussion on the efficiency of each method is provided.

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Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools

Cited by 112 publications

References 26 publications

An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

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Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools

Cited by 112 publications

References 26 publications

An Improved Dual‐Kurtogram‐Based T2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

An Improved Dual‐Kurtogram‐Based T2 Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

Selection of the Informative Frequency Band in a Bearing Fault Diagnosis in the Presence of Non-Gaussian Noise—Comparison of Recently Developed Methods

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An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings

An Improved Dual‐Kurtogram‐Based T² Control Chart for Condition Monitoring and Compound Fault Diagnosis of Rolling Bearings